Run Linear-Linear and Log-Log Regression

Run linear-linear and log-Log regression models using the data. Determine which regression model you would use. Why? The most suitable regression model to use is the liner-liner regression model. This is because it is the most suitable model for the relationships between the variables and it is the model that yields an almost straight-line relationship on the graph plots.

The reason as to why the log-log regression cannot even be used in this work is because some independent variables like Display and Feature have zero (0) values and it is not possible to compute the log value of 0 (zero).It therefore becomes automatic that the moist suitable model to use is the linear-linear regression model which yield the following graph and correlation coefficients. Model Summary Model R R Square Adjusted R. Square Std. Error of the Estimate 1 .490a .240 .239 5.4473518E3 a.

Predictors: (Constant), Feature, Price, Display ANOVAb Model Sum of Squares df Mean Square F Sig. Regression 2.721E10 3 9.069E9 .000a Residual 8.629E10 2.967E7 Total 1.135E11 a. Predictors: (Constant), Feature, Price, Display b. Dependent Variable: Sales Coefficientsa Model Unstandardized Coefficients Standardized Coefficients t Sig. B Std. Error Beta 1 (Constant) 1.923 .055 Price .041 2.417 .016 Display .253 13.072 .000 Feature .315 16.321 .000 a. Dependent Variable: Sales The graph of the variables and sales levels (b) Interpret the regression results from the model you chose. Specifically, discuss the effectiveness of using price promotion, feature and display based on your results.

It would be useful to provide some numerical examples. Price promotion The effectiveness of using price promotion is noted to be the least since it has the least coefficient (0.041). In regard to distribution, the graph indicates that the price brings in the highest level of sale but for a small/percentage of sales. This means that the use of price promotion as a marketing mix variable does not contribute much sales for of the brands. Display The effectiveness of using display as a marketing mix variable is average.

It has a coefficient of 0.253. This is in regard to all of the brands. Feature The effectiveness of using feature as a marketing mix variable is noted to be the most efficient for all of the brands. This is due to its high level of significance (0.315).This means that it is the variable which should be perfected by all of the brands (c) Store 4 is the largest store in the market with more than 40% of market share (see "Statistics").

As a brand manager for Coca-Cola, you are not happy about the fact that sales of Coca-Cola lag behind Pepsi, your major competitor, in the store. Can you explain why this happens? Also, can you recommend how to stimulate sales in store 4 by marketing more effectively? It would be useful to provide some numerical examples. Why Coca-Cola lags behind Pepsi in sales A review of the statistical variable outcomes reveals that the most important.